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pro vyhledávání: '"Luan, Xiaokun"'
Model reuse techniques can reduce the resource requirements for training high-performance deep neural networks (DNNs) by leveraging existing models. However, unauthorized reuse and replication of DNNs can lead to copyright infringement and economic l
Externí odkaz:
http://arxiv.org/abs/2407.03883
Autor:
Cai, Yufan, Hou, Zhe, Luan, Xiaokun, Baena, David Miguel Sanan, Lin, Yun, Sun, Jun, Dong, Jin Song
Program refinement involves correctness-preserving transformations from formal high-level specification statements into executable programs. Traditional verification tool support for program refinement is highly interactive and lacks automation. On t
Externí odkaz:
http://arxiv.org/abs/2406.18616
Publikováno v:
Dianzi Jishu Yingyong, Vol 45, Iss 8, Pp 48-52 (2019)
For high-performance chip designs with ever-increasing scale and increasing operating frequency, performance has always been the focus and difficulty of physical design. The buffer is inserted to minimize signal line delay, which optimizes timing and
Externí odkaz:
https://doaj.org/article/999026a0b9d84d1c82983423edbc1189